# TOM-Net: Learning Transparent Object Matting from a Single Image

arXiv: https://arxiv.org/abs/1803.04636 · HTML: https://mlsanity.com/paper/1803.04636/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 30 of 34)

- [Semantic Image Matting](https://mlsanity.com/paper/2104.08201/) (2021)
- [ClearPose: Large-scale Transparent Object Dataset and Benchmark](https://mlsanity.com/paper/2203.03890/) (2022)
- [Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects](https://mlsanity.com/paper/2110.00087/) (2021)
- [Glass Segmentation with RGB-Thermal Image Pairs](https://mlsanity.com/paper/2204.05453/) (2022)
- [Attention-guided Temporally Coherent Video Object Matting](https://mlsanity.com/paper/2105.11427/) (2021)
- [RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation](https://mlsanity.com/paper/2307.06099/) (2023)
- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [Hybrid Mesh-neural Representation for 3D Transparent Object Reconstruction](https://mlsanity.com/paper/2203.12613/) (2022)
- [TRansPose: Large-Scale Multispectral Dataset for Transparent Object](https://mlsanity.com/paper/2307.05016/) (2023)
- [NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects](https://mlsanity.com/paper/2303.11963/) (2023)
- [Segmenting Transparent Object in the Wild with Transformer](https://mlsanity.com/paper/2101.08461/) (2021)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow](https://mlsanity.com/paper/2409.05688/) (2024)
- [Self-supervised Transparent Liquid Segmentation for Robotic Pouring](https://mlsanity.com/paper/2203.01538/) (2022)
- [NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing](https://mlsanity.com/paper/2303.11219/) (2023)
- [Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes](https://mlsanity.com/paper/2412.01240/) (2024)
- [Seeing and Seeing Through the Glass: Real and Synthetic Data for Multi-Layer Depth Estimation](https://mlsanity.com/paper/2503.11633/) (2025)
- [FakeMix Augmentation Improves Transparent Object Detection](https://mlsanity.com/paper/2103.13279/) (2021)
- [RFTrans: Leveraging Refractive Flow of Transparent Objects for Surface Normal Estimation and Manipulation](https://mlsanity.com/paper/2311.12398/) (2023)
- [Deep Learning in Concealed Dense Prediction](https://mlsanity.com/paper/2504.10979/) (2025)
- [Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds](https://mlsanity.com/paper/2211.16693/) (2022)
- [Virtual Multi-Modality Self-Supervised Foreground Matting for Human-Object Interaction](https://mlsanity.com/paper/2110.03278/) (2021)
- [Situational Perception Guided Image Matting](https://mlsanity.com/paper/2204.09276/) (2022)
- [UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors](https://mlsanity.com/paper/2605.00658/) (2026)
- [Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset](https://mlsanity.com/paper/2109.07577/) (2021)
- [Exploring the Interactive Guidance for Unified and Effective Image Matting](https://mlsanity.com/paper/2205.08324/) (2022)
- [A Generative Adversarial Framework for Optimizing Image Matting and Harmonization Simultaneously](https://mlsanity.com/paper/2108.06087/) (2021)
- [Grasping the Inconspicuous](https://mlsanity.com/paper/2211.08182/) (2022)
- [Transparent Object Tracking with Enhanced Fusion Module](https://mlsanity.com/paper/2309.06701/) (2023)
- [EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects for Robotic Manipulation](https://mlsanity.com/paper/2312.13906/) (2023)

## Abstract (arXiv)

This paper addresses the problem of transparent object matting. Existing image matting approaches for transparent objects often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we first formulate transparent object matting as a refractive flow estimation problem. We then propose a deep learning framework, called TOM-Net, for learning the refractive flow. Our framework comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement. At test time, TOM-Net takes a single image as input, and outputs a matte (consisting of an object mask, an attenuation mask and a refractive flow field) in a fast feed-forward pass. As no off-the-shelf dataset is available for transparent object matting, we create a large-scale synthetic dataset consisting of 158K images of transparent objects rendered in front of images sampled from the Microsoft COCO dataset. We also collect a real dataset consisting of 876 samples using 14 transparent objects and 60 background images. Promising experimental results have been achieved on both synthetic and real data, which clearly demonstrate the effectiveness of our approach.